The Markov condition ensures every independence relation acquired by implying \(d\) -separation on the causal graph holds in the joint probabilistic distribution. The Causal Markov Condition states that a node is independent of all other nodes except its direct cause and effects, conditioning on all of its direct cause variables [1]}. The causal Markov condition and Markov condition are equivalent if the Bayesian network is a causal graph.
